In the published article, there were errors in affiliations 1 and 5. Instead of “School of Electrical Engineering and Computer Science, Handong Global University, Pohang-si, Republic of Korea” and “AI Graudate School, Gwangju Institute of Science and Technology, Gwangu, Republic of Korea”, it should be “Department of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea” and “AI Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea”.
In the original article, there were errors in Table 1. The mark “*” was incorrectly used for the dataset “Cho et al., 2017*” in the column “References”, the “*” should be removed. The Num. of electrodes of the dataset “Zhou, 2020” was “26,41” and the sampling rate of the dataset “Ahn et al., 2013a” was “512,500” which may cause misreading. The texts should be “Cho et al., 2017”, “512, 500” and “26, 41”. The corrected Table 1 appears below.
Table 1
| Public specifications | Environmental specifications | Essential specifications | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| References | Resources | Num. of citations | Device | Num. of electrodes | Extra electrode | Electrode setting | Sampling rate (Hz) | Data format | Signal continuity | Event type | Event latency | Channels | |
| Motor imagery | Stieger et al., 2021 | Scientific data | 11 | Neuroscan SynAmps | 64 | Cursor | 10-10 | 1,000 | mat | x | o | o | o |
| Motor imagery, Motor execution | Jeong et al., 2020* | Deep BCI, Gigascience | 25 | BrainProduct BrainAmp | 60 | EOG, EMG | – | 2,500 | mat | o | o | o | o |
| Motor imagery | Zhou, 2020 | IEEE DataPort | – | Neuroscan SynAmps2 | 26, 41 | EOG | – | 500 | npz | o | o | o | o |
| Wu, 2020 | IEEE DataPort | – | Neuroscan SynAmps2 | 122 | ear-EEG | 10-20 | 1,000 | dat | o | o | o | o | |
| Ma et al., 2020 | Scientific data | 11 | Neuroscan SynAmps2 | 64 | EOG, EMG | – | 1,000 | mat, cnt | o | o | o | o | |
| Lee et al., 2019 | Deep BCI, Gigascience, MOABB | 171 | BrainProduct BrainAmp | 62 | EMG | 10–20 | 1,000 | mat | o | o | o | o | |
| Kim et al., 2018 | Deep BCI | 113 | BrainProduct BrainAmp | 30 | – | – | 250 | vhdr | o | o | o | o | |
| Motor imagery, Motor execution | Kaya et al., 2018* | Scientific data | 84 | Neurofax EEG-1200 | 19 | – | 10–20 | 200 | mat | o | o | o | o |
| Ofner et al., 2017* | BNCI Horizon, MOABB | 147 | g.tec USBamp | 61 | EOG, EMG | – | 512 | gdf | o | o | o | o | |
| Motor imagery | Cho et al., 2017 | Deep BCI, MOABB, Gigascience | 172 | Biosemi | 64 | EMG | 10–20 | 512 | mat | x | o | o | o |
| Lee et al., 2016 | Deep BCI | 14 | BrainProduct BrainAmp | 70 | EOG, EMG | 10–20 | 1,000 | vhdr | o | x | o | o | |
| Shin et al., 2017 | MOABB | 135 | BrainProduct BrainAmp | 30 | NIRS, EOG, ECG | 10–5 | 1,000 | mat | o | o | o | o | |
| Zhou et al., 2016 | MOABB | 32 | – | 14 | – | 10–20 | 250 | cnt | o | x | o | o | |
| Steyrl et al., 2016 | BNCI Horizon, MOABB | 93 | g.tec USBamp | 15 | – | 10–10 | 512 | mat | o | o | o | x | |
| Yi et al., 2014 | MOABB | 46 | Neuroscan SynAmps2 | 64 | – | 10–20 | 1,000 | mat | x | o | x | x | |
| Ahn et al., 2013a | Deep BCI | 82 | Biosemi, BrainProduct BrainAmp | 19 | – | 10–10 | 512, 500 | mat | Δ | Δ | Δ | Δ | |
| Faller et al., 2012 | BNCI Horizon, MOABB | 138 | g.tec USBamp | 13 | – | 10–5 | 512 | mat | o | o | o | x | |
| Tangermann et al., 2012 | BNCI Horizon, MOABB | 652 | - | 22 | EOG | 10–20 | 250 | gdf | o | o | o | o | |
| Grosse-Wentrup et al., 2009 | MOABB | 178 | BrainProduct BrainAmp | 128 | – | 10–20 | 500 | set | o | o | o | o | |
| Leeb et al., 2007 | BNCI Horizon, MOABB | 486 | g.tec Usama | 3 (Central) | EOG | – | 250 | mat | o | o | o | o | |
| Motor imagery, Motor execution | Schalk et al., 2004* | MOABB | 2,915 | – | 64 | – | 10–20 | 160 | edf | o | o | o | o |
| Motor execution | Schwarz et al., 2020 | BNCI Horizon | 19 | g.tec USBamp | 58 | EOG, Force-sensing resistor sensor | – | 256 | mat | o | o | o | o |
| Schwarz et al., 2020 | BNCI Horizon | 19 | EEG-VersatileTM system | 32 | EOG, photodiode sensor | – | 256 | mat | o | o | o | o | |
| Schwarz et al., 2020 | BNCI Horizon | 19 | EEG-HeroTM headset | 11 | photodiode sensor | 10–20 | 256 | mat | o | o | o | o | |
| Wagner et al., 2019 | Scientific data | 8 | g.tec USBamp | 108 | EMG, EOG, goniometers | 10–20 | 512 | set | o | o | o | o | |
| Brantley et al., 2018 | Scientific data | 20 | BrainProduct BrainAmp | 60 | EOG, EMG | 10–20 | 1,000 | mat | o | – | – | o | |
| Luciw et al., 2014 | Scientific data | 87 | BrainProduct BrainAmp | 64 | EMG | – | 500 | mat | o | – | – | o | |
Public and environmental specifications of motor imagery/execution datasets (–: no information provided).
For essential specifications, o: satisfied, Δ: partially satisfied and x: unsatisfied.
*These datasets contain both motor imagery and execution paradigm.
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Statements
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Summary
Keywords
brain-computer interface (BCI), motor imagery, motor execution, public dataset, data quality, meta-analysis
Citation
Gwon D, Won K, Song M, Nam CS, Jun SC and Ahn M (2023) Corrigendum: Review of public motor imagery and execution datasets in brain-computer interfaces. Front. Hum. Neurosci. 17:1205419. doi: 10.3389/fnhum.2023.1205419
Received
13 April 2023
Accepted
03 May 2023
Published
17 May 2023
Volume
17 - 2023
Edited and reviewed by
Gernot R. Müller-Putz, Graz University of Technology, Austria
Updates
Copyright
© 2023 Gwon, Won, Song, Nam, Jun and Ahn.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Minkyu Ahn minkyuahn@handong.edu
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.